name: seaborn description: "Seaborn Statistical Visualization workflow skill. Use this skill when the user needs Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off." version: "0.0.1" category: development tags: ["seaborn", "python", "visualization", "library", "for", "creating", "publication-quality", "statistical"] complexity: advanced risk: safe tools: ["codex-cli", "claude-code", "cursor", "gemini-cli", "opencode"] source: community author: "sickn33" date_added: "2026-04-15" date_updated: "2026-04-25"

Seaborn Statistical Visualization

Overview

This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/seaborn from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Seaborn Statistical Visualization

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Design Philosophy, Core Plotting Interfaces, Plotting Functions by Category, Multi-Plot Grids, Figure-Level vs Axes-Level Functions, Data Structure Requirements.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• You need publication-quality statistical graphics directly from tabular datasets.
• You are exploring multivariate relationships, distributions, or grouped comparisons with minimal plotting code.
• You want seaborn's dataset-oriented API and statistical defaults on top of matplotlib.
• Use when the request clearly matches the imported source intent: Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex....
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: Overview

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Imported: Design Philosophy

Seaborn follows these core principles:

1. Dataset-oriented: Work directly with DataFrames and named variables rather than abstract coordinates
2. Semantic mapping: Automatically translate data values into visual properties (colors, sizes, styles)
3. Statistical awareness: Built-in aggregation, error estimation, and confidence intervals
4. Aesthetic defaults: Publication-ready themes and color palettes out of the box
5. Matplotlib integration: Full compatibility with matplotlib customization when needed

Examples

Example 1: Ask for the upstream workflow directly

Use @seaborn to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @seaborn against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @seaborn for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @seaborn using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: Quick Start

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# Load example dataset
df = sns.load_dataset('tips')

# Create a simple visualization
sns.scatterplot(data=df, x='total_bill', y='tip', hue='day')
plt.show()

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Data Preparation Always use well-structured DataFrames with meaningful column names: `python # Good: Named columns in DataFrame df = pd.DataFrame({'bill': bills, 'tip': tips, 'day': days}) sns.scatterplot(data=df, x='bill', y='tip', hue='day') # Avoid: Unnamed arrays sns.scatterplot(x=xarray, y=yarray) # Loses axis labels ### 2.
• Choose the Right Plot Type Continuous x, continuous y: scatterplot, lineplot, kdeplot, regplot Continuous x, categorical y: violinplot, boxplot, stripplot, swarmplot One continuous variable: histplot, kdeplot, ecdfplot Correlations/matrices: heatmap, clustermap Pairwise relationships: pairplot, jointplot ### 3.
• Use Figure-Level Functions for Faceting python # Instead of manual subplot creation sns.relplot(data=df, x='x', y='y', col='category', colwrap=3) # Not: Creating subplots manually for simple faceting ### 4.
• Leverage Semantic Mappings Use hue, size, and style to encode additional dimensions: python sns.scatterplot(data=df, x='x', y='y', hue='category', # Color by category size='importance', # Size by continuous variable style='type') # Marker style by type ### 5.
• Control Statistical Estimation Many functions compute statistics automatically.
• Understand and customize: python # Lineplot computes mean and 95% CI by default sns.lineplot(data=df, x='time', y='value', errorbar='sd') # Use standard deviation instead # Barplot computes mean by default sns.barplot(data=df, x='category', y='value', estimator='median', # Use median instead errorbar=('ci', 95)) # Bootstrapped CI ### 6.
• Combine with Matplotlib Seaborn integrates seamlessly with matplotlib for fine-tuning: python ax = sns.scatterplot(data=df, x='x', y='y') ax.set(xlabel='Custom X Label', ylabel='Custom Y Label', title='Custom Title') ax.axhline(y=0, color='r', linestyle='--') plt.tightlayout() ### 7.

Imported Operating Notes

Imported: Best Practices

1. Data Preparation

Always use well-structured DataFrames with meaningful column names:

# Good: Named columns in DataFrame
df = pd.DataFrame({'bill': bills, 'tip': tips, 'day': days})
sns.scatterplot(data=df, x='bill', y='tip', hue='day')

# Avoid: Unnamed arrays
sns.scatterplot(x=x_array, y=y_array)  # Loses axis labels

2. Choose the Right Plot Type

Continuous x, continuous y: scatterplot, lineplot, kdeplot, regplot Continuous x, categorical y: violinplot, boxplot, stripplot, swarmplot One continuous variable: histplot, kdeplot, ecdfplot Correlations/matrices: heatmap, clustermap Pairwise relationships: pairplot, jointplot

3. Use Figure-Level Functions for Faceting

# Instead of manual subplot creation
sns.relplot(data=df, x='x', y='y', col='category', col_wrap=3)

# Not: Creating subplots manually for simple faceting

4. Leverage Semantic Mappings

Use hue, size, and style to encode additional dimensions:

sns.scatterplot(data=df, x='x', y='y',
                hue='category',      # Color by category
                size='importance',    # Size by continuous variable
                style='type')         # Marker style by type

5. Control Statistical Estimation

Many functions compute statistics automatically. Understand and customize:

# Lineplot computes mean and 95% CI by default
sns.lineplot(data=df, x='time', y='value',
             errorbar='sd')  # Use standard deviation instead

# Barplot computes mean by default
sns.barplot(data=df, x='category', y='value',
            estimator='median',  # Use median instead
            errorbar=('ci', 95))  # Bootstrapped CI

6. Combine with Matplotlib

Seaborn integrates seamlessly with matplotlib for fine-tuning:

ax = sns.scatterplot(data=df, x='x', y='y')
ax.set(xlabel='Custom X Label', ylabel='Custom Y Label',
       title='Custom Title')
ax.axhline(y=0, color='r', linestyle='--')
plt.tight_layout()

7. Save High-Quality Figures

fig = sns.relplot(data=df, x='x', y='y', col='group')
fig.savefig('figure.png', dpi=300, bbox_inches='tight')
fig.savefig('figure.pdf')  # Vector format for publications

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/seaborn, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Imported Troubleshooting Notes

Imported: Troubleshooting

Issue: Legend Outside Plot Area

Figure-level functions place legends outside by default. To move inside:

g = sns.relplot(data=df, x='x', y='y', hue='category')
g._legend.set_bbox_to_anchor((0.9, 0.5))  # Adjust position

Issue: Overlapping Labels

plt.xticks(rotation=45, ha='right')
plt.tight_layout()

Issue: Figure Too Small

For figure-level functions:

sns.relplot(data=df, x='x', y='y', height=6, aspect=1.5)

For axes-level functions:

fig, ax = plt.subplots(figsize=(10, 6))
sns.scatterplot(data=df, x='x', y='y', ax=ax)

Issue: Colors Not Distinct Enough

# Use a different palette
sns.set_palette("bright")

# Or specify number of colors
palette = sns.color_palette("husl", n_colors=len(df['category'].unique()))
sns.scatterplot(data=df, x='x', y='y', hue='category', palette=palette)

Issue: KDE Too Smooth or Jagged

# Adjust bandwidth
sns.kdeplot(data=df, x='x', bw_adjust=0.5)  # Less smooth
sns.kdeplot(data=df, x='x', bw_adjust=2)    # More smooth

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: Resources

This skill includes reference materials for deeper exploration:

references/

• function_reference.md - Comprehensive listing of all seaborn functions with parameters and examples
• objects_interface.md - Detailed guide to the modern seaborn.objects API
• examples.md - Common use cases and code patterns for different analysis scenarios

Load reference files as needed for detailed function signatures, advanced parameters, or specific examples.

Imported: Core Plotting Interfaces

Function Interface (Traditional)

The function interface provides specialized plotting functions organized by visualization type. Each category has axes-level functions (plot to single axes) and figure-level functions (manage entire figure with faceting).

When to use:

• Quick exploratory analysis
• Single-purpose visualizations
• When you need a specific plot type

Objects Interface (Modern)

The seaborn.objects interface provides a declarative, composable API similar to ggplot2. Build visualizations by chaining methods to specify data mappings, marks, transformations, and scales.

When to use:

• Complex layered visualizations
• When you need fine-grained control over transformations
• Building custom plot types
• Programmatic plot generation

from seaborn import objects as so

# Declarative syntax
(
    so.Plot(data=df, x='total_bill', y='tip')
    .add(so.Dot(), color='day')
    .add(so.Line(), so.PolyFit())
)

Imported: Plotting Functions by Category

Relational Plots (Relationships Between Variables)

Use for: Exploring how two or more variables relate to each other

• scatterplot() - Display individual observations as points
• lineplot() - Show trends and changes (automatically aggregates and computes CI)
• relplot() - Figure-level interface with automatic faceting

Key parameters:

• x, y - Primary variables
• hue - Color encoding for additional categorical/continuous variable
• size - Point/line size encoding
• style - Marker/line style encoding
• col, row - Facet into multiple subplots (figure-level only)

# Scatter with multiple semantic mappings
sns.scatterplot(data=df, x='total_bill', y='tip',
                hue='time', size='size', style='sex')

# Line plot with confidence intervals
sns.lineplot(data=timeseries, x='date', y='value', hue='category')

# Faceted relational plot
sns.relplot(data=df, x='total_bill', y='tip',
            col='time', row='sex', hue='smoker', kind='scatter')

Distribution Plots (Single and Bivariate Distributions)

Use for: Understanding data spread, shape, and probability density

• histplot() - Bar-based frequency distributions with flexible binning
• kdeplot() - Smooth density estimates using Gaussian kernels
• ecdfplot() - Empirical cumulative distribution (no parameters to tune)
• rugplot() - Individual observation tick marks
• displot() - Figure-level interface for univariate and bivariate distributions
• jointplot() - Bivariate plot with marginal distributions
• pairplot() - Matrix of pairwise relationships across dataset

Key parameters:

• x, y - Variables (y optional for univariate)
• hue - Separate distributions by category
• stat - Normalization: "count", "frequency", "probability", "density"
• bins / binwidth - Histogram binning control
• bw_adjust - KDE bandwidth multiplier (higher = smoother)
• fill - Fill area under curve
• multiple - How to handle hue: "layer", "stack", "dodge", "fill"

# Histogram with density normalization
sns.histplot(data=df, x='total_bill', hue='time',
             stat='density', multiple='stack')

# Bivariate KDE with contours
sns.kdeplot(data=df, x='total_bill', y='tip',
            fill=True, levels=5, thresh=0.1)

# Joint plot with marginals
sns.jointplot(data=df, x='total_bill', y='tip',
              kind='scatter', hue='time')

# Pairwise relationships
sns.pairplot(data=df, hue='species', corner=True)

Categorical Plots (Comparisons Across Categories)

Use for: Comparing distributions or statistics across discrete categories

Categorical scatterplots:

• stripplot() - Points with jitter to show all observations
• swarmplot() - Non-overlapping points (beeswarm algorithm)

Distribution comparisons:

• boxplot() - Quartiles and outliers
• violinplot() - KDE + quartile information
• boxenplot() - Enhanced boxplot for larger datasets

Statistical estimates:

• barplot() - Mean/aggregate with confidence intervals
• pointplot() - Point estimates with connecting lines
• countplot() - Count of observations per category

Figure-level:

• catplot() - Faceted categorical plots (set kind parameter)

Key parameters:

• x, y - Variables (one typically categorical)
• hue - Additional categorical grouping
• order, hue_order - Control category ordering
• dodge - Separate hue levels side-by-side
• orient - "v" (vertical) or "h" (horizontal)
• kind - Plot type for catplot: "strip", "swarm", "box", "violin", "bar", "point"

# Swarm plot showing all points
sns.swarmplot(data=df, x='day', y='total_bill', hue='sex')

# Violin plot with split for comparison
sns.violinplot(data=df, x='day', y='total_bill',
               hue='sex', split=True)

# Bar plot with error bars
sns.barplot(data=df, x='day', y='total_bill',
            hue='sex', estimator='mean', errorbar='ci')

# Faceted categorical plot
sns.catplot(data=df, x='day', y='total_bill',
            col='time', kind='box')

Regression Plots (Linear Relationships)

Use for: Visualizing linear regressions and residuals

• regplot() - Axes-level regression plot with scatter + fit line
• lmplot() - Figure-level with faceting support
• residplot() - Residual plot for assessing model fit

Key parameters:

• x, y - Variables to regress
• order - Polynomial regression order
• logistic - Fit logistic regression
• robust - Use robust regression (less sensitive to outliers)
• ci - Confidence interval width (default 95)
• scatter_kws, line_kws - Customize scatter and line properties

# Simple linear regression
sns.regplot(data=df, x='total_bill', y='tip')

# Polynomial regression with faceting
sns.lmplot(data=df, x='total_bill', y='tip',
           col='time', order=2, ci=95)

# Check residuals
sns.residplot(data=df, x='total_bill', y='tip')

Matrix Plots (Rectangular Data)

Use for: Visualizing matrices, correlations, and grid-structured data

• heatmap() - Color-encoded matrix with annotations
• clustermap() - Hierarchically-clustered heatmap

Key parameters:

• data - 2D rectangular dataset (DataFrame or array)
• annot - Display values in cells
• fmt - Format string for annotations (e.g., ".2f")
• cmap - Colormap name
• center - Value at colormap center (for diverging colormaps)
• vmin, vmax - Color scale limits
• square - Force square cells
• linewidths - Gap between cells

# Correlation heatmap
corr = df.corr()
sns.heatmap(corr, annot=True, fmt='.2f',
            cmap='coolwarm', center=0, square=True)

# Clustered heatmap
sns.clustermap(data, cmap='viridis',
               standard_scale=1, figsize=(10, 10))

Imported: Multi-Plot Grids

Seaborn provides grid objects for creating complex multi-panel figures:

FacetGrid

Create subplots based on categorical variables. Most useful when called through figure-level functions (relplot, displot, catplot), but can be used directly for custom plots.

g = sns.FacetGrid(df, col='time', row='sex', hue='smoker')
g.map(sns.scatterplot, 'total_bill', 'tip')
g.add_legend()

PairGrid

Show pairwise relationships between all variables in a dataset.

g = sns.PairGrid(df, hue='species')
g.map_upper(sns.scatterplot)
g.map_lower(sns.kdeplot)
g.map_diag(sns.histplot)
g.add_legend()

JointGrid

Combine bivariate plot with marginal distributions.

g = sns.JointGrid(data=df, x='total_bill', y='tip')
g.plot_joint(sns.scatterplot)
g.plot_marginals(sns.histplot)

Imported: Figure-Level vs Axes-Level Functions

Understanding this distinction is crucial for effective seaborn usage:

Axes-Level Functions

• Plot to a single matplotlib Axes object
• Integrate easily into complex matplotlib figures
• Accept ax= parameter for precise placement
• Return Axes object
• Examples: scatterplot, histplot, boxplot, regplot, heatmap

When to use:

• Building custom multi-plot layouts
• Combining different plot types
• Need matplotlib-level control
• Integrating with existing matplotlib code

fig, axes = plt.subplots(2, 2, figsize=(10, 10))
sns.scatterplot(data=df, x='x', y='y', ax=axes[0, 0])
sns.histplot(data=df, x='x', ax=axes[0, 1])
sns.boxplot(data=df, x='cat', y='y', ax=axes[1, 0])
sns.kdeplot(data=df, x='x', y='y', ax=axes[1, 1])

Figure-Level Functions

• Manage entire figure including all subplots
• Built-in faceting via col and row parameters
• Return FacetGrid, JointGrid, or PairGrid objects
• Use height and aspect for sizing (per subplot)
• Cannot be placed in existing figure
• Examples: relplot, displot, catplot, lmplot, jointplot, pairplot

When to use:

• Faceted visualizations (small multiples)
• Quick exploratory analysis
• Consistent multi-panel layouts
• Don't need to combine with other plot types

# Automatic faceting
sns.relplot(data=df, x='x', y='y', col='category', row='group',
            hue='type', height=3, aspect=1.2)

Imported: Data Structure Requirements

Long-Form Data (Preferred)

Each variable is a column, each observation is a row. This "tidy" format provides maximum flexibility:

# Long-form structure
   subject  condition  measurement
0        1    control         10.5
1        1  treatment         12.3
2        2    control          9.8
3        2  treatment         13.1

Advantages:

• Works with all seaborn functions
• Easy to remap variables to visual properties
• Supports arbitrary complexity
• Natural for DataFrame operations

Wide-Form Data

Variables are spread across columns. Useful for simple rectangular data:

# Wide-form structure
   control  treatment
0     10.5       12.3
1      9.8       13.1

Use cases:

• Simple time series
• Correlation matrices
• Heatmaps
• Quick plots of array data

Converting wide to long:

df_long = df.melt(var_name='condition', value_name='measurement')

Imported: Color Palettes

Seaborn provides carefully designed color palettes for different data types:

Qualitative Palettes (Categorical Data)

Distinguish categories through hue variation:

• "deep" - Default, vivid colors
• "muted" - Softer, less saturated
• "pastel" - Light, desaturated
• "bright" - Highly saturated
• "dark" - Dark values
• "colorblind" - Safe for color vision deficiency

sns.set_palette("colorblind")
sns.color_palette("Set2")

Sequential Palettes (Ordered Data)

Show progression from low to high values:

• "rocket", "mako" - Wide luminance range (good for heatmaps)
• "flare", "crest" - Restricted luminance (good for points/lines)
• "viridis", "magma", "plasma" - Matplotlib perceptually uniform

sns.heatmap(data, cmap='rocket')
sns.kdeplot(data=df, x='x', y='y', cmap='mako', fill=True)

Diverging Palettes (Centered Data)

Emphasize deviations from a midpoint:

• "vlag" - Blue to red
• "icefire" - Blue to orange
• "coolwarm" - Cool to warm
• "Spectral" - Rainbow diverging

sns.heatmap(correlation_matrix, cmap='vlag', center=0)

Custom Palettes

# Create custom palette
custom = sns.color_palette("husl", 8)

# Light to dark gradient
palette = sns.light_palette("seagreen", as_cmap=True)

# Diverging palette from hues
palette = sns.diverging_palette(250, 10, as_cmap=True)

Imported: Theming and Aesthetics

Set Theme

set_theme() controls overall appearance:

# Set complete theme
sns.set_theme(style='whitegrid', palette='pastel', font='sans-serif')

# Reset to defaults
sns.set_theme()

Styles

Control background and grid appearance:

• "darkgrid" - Gray background with white grid (default)
• "whitegrid" - White background with gray grid
• "dark" - Gray background, no grid
• "white" - White background, no grid
• "ticks" - White background with axis ticks

sns.set_style("whitegrid")

# Remove spines
sns.despine(left=False, bottom=False, offset=10, trim=True)

# Temporary style
with sns.axes_style("white"):
    sns.scatterplot(data=df, x='x', y='y')

Contexts

Scale elements for different use cases:

• "paper" - Smallest (default)
• "notebook" - Slightly larger
• "talk" - Presentation slides
• "poster" - Large format

sns.set_context("talk", font_scale=1.2)

# Temporary context
with sns.plotting_context("poster"):
    sns.barplot(data=df, x='category', y='value')

Imported: Common Patterns

Exploratory Data Analysis

# Quick overview of all relationships
sns.pairplot(data=df, hue='target', corner=True)

# Distribution exploration
sns.displot(data=df, x='variable', hue='group',
            kind='kde', fill=True, col='category')

# Correlation analysis
corr = df.corr()
sns.heatmap(corr, annot=True, cmap='coolwarm', center=0)

Publication-Quality Figures

sns.set_theme(style='ticks', context='paper', font_scale=1.1)

g = sns.catplot(data=df, x='treatment', y='response',
                col='cell_line', kind='box', height=3, aspect=1.2)
g.set_axis_labels('Treatment Condition', 'Response (μM)')
g.set_titles('{col_name}')
sns.despine(trim=True)

g.savefig('figure.pdf', dpi=300, bbox_inches='tight')

Complex Multi-Panel Figures

# Using matplotlib subplots with seaborn
fig, axes = plt.subplots(2, 2, figsize=(12, 10))

sns.scatterplot(data=df, x='x1', y='y', hue='group', ax=axes[0, 0])
sns.histplot(data=df, x='x1', hue='group', ax=axes[0, 1])
sns.violinplot(data=df, x='group', y='y', ax=axes[1, 0])
sns.heatmap(df.pivot_table(values='y', index='x1', columns='x2'),
            ax=axes[1, 1], cmap='viridis')

plt.tight_layout()

Time Series with Confidence Bands

# Lineplot automatically aggregates and shows CI
sns.lineplot(data=timeseries, x='date', y='measurement',
             hue='sensor', style='location', errorbar='sd')

# For more control
g = sns.relplot(data=timeseries, x='date', y='measurement',
                col='location', hue='sensor', kind='line',
                height=4, aspect=1.5, errorbar=('ci', 95))
g.set_axis_labels('Date', 'Measurement (units)')

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.